brand-design-language-reverse

brand-design-language-reverse is a skill for Claude Code from aAAaqwq/AGI-Super-Team. It costs 263 tokens per session (2,974 once invoked), scanned A, original, MIT.

A website analysis guide that examines a public URL and turns its visible colours, fonts, components, icons, animations, and detected framework into design rules and reusable CSS values.

In plain words
What is it for?
Use it to study a brand or competitor, plan a landing page or dashboard, identify likely web technologies, and create a starting design system for your own project.
Why use it?
It replaces guesswork when you want to understand or reproduce a site's visual style. It also separates observed evidence from assumptions and avoids inspecting private internals.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agi-super-team plugin — 194 skills, 1 agent shipped together

Good fit Use it to study a brand or competitor, plan a landing page or dashboard, identify likely web technologies, and create a starting design system for your own project.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aaaaqwq/agi-super-team/brand-design-language-reverse
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add aAAaqwq/AGI-Super-Team --skill brand-design-language-reverse
Clone the repo
git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team

Made for: Claude Code.

Or install agi-super-team, the plugin that ships this one along with the rest of its 194 skills, 1 agent.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for brand-design-language-reverse

README.md
[![agentmods](https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/brand-design-language-reverse/github.svg)](https://agentmods.dev/skills/aaaaqwq/agi-super-team/brand-design-language-reverse)
Your own site
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/brand-design-language-reverse"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/brand-design-language-reverse/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for brand-design-language-reverse

Your own site · 80×15
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/brand-design-language-reverse"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/brand-design-language-reverse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 263 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,974 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00263 $0.02974
Opus 5 $0.00131 $0.01487
Sonnet 5 $0.00053 $0.00595
Haiku 4.5 $0.00026 $0.00297

Measured yesterday against content hash 7e208888822e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

brand-design-language-reverse scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

Use `WebFetch` (or `curl` for raw inspection) on the public page.
skills/brand-design-language-reverse/SKILL.md · 248 lines

How it starts

The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Brand Design Language Reverse

Turn any live site into a copy-paste design system. Reads a real URL, extracts what is observable (colors, fonts, component patterns, icon/animation technique, framework), and outputs a five-part report ending in usable CSS tokens. Analysis + delivery in one pass.

When to Use

  • You admire a brand/product and want to recreate its visual feel in your own project (landing, dashboard, component).
  • You need a competitor's palette/type/components as a reference before designing.
  • Someone asks "what tech/framework does that site use?" or "how did they do that animation/icon?".
  • You are starting UI work and want a grounded, real-world reference instead of guessing at colors and patterns.

Core Principles

  1. Evidence over guessing. Distinguish three confidence levels in every report (see grading below). Never present an industry-known value as if you scraped it, and never invent values.
  2. Observe, don't reverse-engineer internals. Inspect the public HTML/CSS/JS the server sends. Do not decompile, do not probe authenticated product internals, do not harvest private endpoints. Marketing/home pages are public and fair game; a logged-in editor is not.
  3. Deliver working output. End every run with a concrete tokens.css / component snippet the user can drop in, not just an analysis essay.

Evidence Grading

Annotate each claim in the report so the user knows what to trust:

Grade Meaning Example
[observed] Read directly from served HTML/CSS this run inline #00b67a present in page styles
[inferred] Reasoned from a visible pattern / public CSS variable gradient palette derived from two observed stops
[known] Industry-public fact (Apple brand blue #0071e3) but NOT read this run mark it clearly as known, not scraped

Workflow: URL → Design Tokens

1. Fetch the target URL

Use WebFetch (or curl for raw inspection) on the public page.

Read the full file on GitHub · 248 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 248 lines · 263 tokens per session scan A 7e208888822e

Subscribe to this mod's changes

brand-design-language-reverse is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 263 tokens to every session and 2,974 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-10.

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